Maximum Batch Frobenius Norm for Multi-Domain Text Classification
This work addresses a specific challenge in multi-domain text classification for researchers and practitioners, offering an incremental improvement over existing adversarial learning methods.
The paper tackles the problem of degraded feature discriminability in multi-domain text classification when using adversarial learning to extract domain-invariant features, and proposes a maximum batch Frobenius norm method that improves state-of-the-art performance on two benchmarks.
Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learning to extract domain-invariant features to yield state-of-the-art results. However, these methods still face one challenge: transforming original features to be domain-invariant distorts the distributions of the original features, degrading the discriminability of the learned features. To address this issue, we first investigate the structure of the batch classification output matrix and theoretically justify that the discriminability of the learned features has a positive correlation with the Frobenius norm of the batch output matrix. Based on this finding, we propose a maximum batch Frobenius norm (MBF) method to boost the feature discriminability for MDTC. Experiments on two MDTC benchmarks show that our MBF approach can effectively advance the performance of the state-of-the-art.